Datenerfassung für die Anwendung lernender Algorithmen bei der Herstellung von Blechformteilen - Purr, Stephan (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
The process of industrially producing shaped sheet metal parts is influenced by a large number of fluctuating factors. Therefore, quality problems appear to occur randomly and cause defective parts and a loss of productivity. A suitable way to improve these processes is the application of data analysis and statistical methods like machine learning, which require a large amount of data. So far, the data base and suitable methods for data acquisition are lacking for the use of these methods. Therefore, in this work, a generally valid and transferable method for recording and saving relevant influencing and resultant variables during the series production of sheet metal parts has been developed. This method and the resulting data are the basis for the analysis of correlations in the process, the prediction of quality by learning algorithms and the calculation of suitable press settings. These use cases were tested in the work and the results were evaluated. Thus, the functionality of the data acquisition and the expected potentials could be confirme